Efficient Noisy Sound-Event Mixture Classification Using Adaptive-Sparse Complex-Valued Matrix Factorization and OvsO SVM.

This paper proposes a solution for events classification from a sole noisy mixture that consist of two major steps: a sound-event separation and a sound-event classification. The traditional complex nonnegative matrix factorization (CMF) is extended by cooperation with the optimal adaptive sparsity to decompose a noisy single-channel mixture. The proposed adaptive sparsity CMF algorithm encodes the spectra pattern and estimates the phase of the original signals in time-frequency representation.